PURPOSE To evaluate the performance of an artificial intelligence (AI) based software solution tested on liver volumetric analyses and to compare the results to the manual contour segmentation. MATERIALS AND METHODS We retrospectively obtained 462 multiphasic CT datasets with six series for each patient three different contrast phases and two slice thickness reconstructions (1.5/5?mm), totaling 2772 series. AI-based liver volumes were determined using multi-scale deep-reinforcement learning for 3D body markers detection and 3D structure segmentation. The algorithm was trained for liver volumetry on approximately 5000 datasets. We computed the absolute error of each automatically- and manually-derived volume relative to the mean manual volume. The mean processing time/dataset and method was recorded. Variations of liver volumes were compared using univariate generalized linear model analyses. A subgroup of 60 datasets was manually segmented by three radiologists, with a further subgroup of 20 segmented three times by each, to compare the automatically-derived results with the ground-truth. RESULTS The mean absolute error of the automatically-derived measurement was 44.3?mL (representing 2.37 % of the averaged liver volumes). The liver volume was neither dependent on the contrast phase (p?=?0.697), nor on the slice thickness (p?=?0.446). The mean processing time/dataset with the algorithm was 9.94?s (sec) compared to manual segmentation with 219.34?s. We found an excellent agreement between both approaches with an ICC value of 0.996. CONCLUSION The results of our study demonstrate that AI-powered fully automated liver volumetric analyses can be done with excellent accuracy, reproducibility, robustness, speed and agreement with the manual segmentation. PURPOSE Magnetic resonance defecography (MRD) was used to evaluate anatomic and functional pelvic floor disorders in women with stress urinary incontinence (SUI) before and after midurethral sling (MUS) intervention. METHOD We performed MRD in both SUI patients and continent controls. Static MR was used to describe the anatomic abnormalities in levator ani muscle and periurethral ligaments (PUL). Dynamic MR was used to depict the function of the urethra and pelvic floor. We compared the MRD parameters between the SUI patients and continent controls before surgery. For SUI patients, dynamic MR images evaluated the functional changes of the urethra and pelvic floor after surgery. RESULTS In SUI group, 75.8 % have PUL defects, 65.7 % discontinuity or complete loss of pubococcygeal muscle, as compared to the continent groups (p 0.05). CONCLUSIONS MRD with high-resolution and defecation phases provides a detailed anatomic and functional evaluation of the pelvic floor in female SUI before and after pelvic reconstruction. PURPOSE To assess the diagnostic accuracy of imaging-based deep learning analysis to differentiate between human papillomavirus (HPV) positive and negative oropharyngeal squamous cell carcinomas (OPSCCs) using FDG-PET images. METHODS One hundred and twenty patients with OPSCC who underwent pretreatment FDG-PET/CT were included and divided into the training 90 patients and validation 30 patients cohorts. In the training session, 2160 FDG-PET images were analyzed after data augmentation process by a deep learning technique to create a diagnostic model to discriminate between HPV-positive and HPV-negative OPSCCs. Validation cohort data were subsequently analyzed for confirmation of diagnostic accuracy in determining HPV status by the deep learning-based diagnosis model. In addition, two radiologists evaluated the validation cohort image-data to determine the HPV status based on each tumor's imaging findings. RESULTS In deep learning analysis with training session, the diagnostic model using training dataset was successfully created. https://www.selleckchem.com/peptide/bulevirtide-myrcludex-b.html In the validation session, the deep learning diagnostic model revealed sensitivity of 0.83, specificity of 0.83, positive predictive value of 0.88, negative predictive value of 0.77, and diagnostic accuracy of 0.83, while the visual assessment by two radiologists revealed 0.78, 0.5, 0.7, 0.6, and 0.67 (reader 1), and 0.56, 0.67, 0.71, 0.5, and 0.6 (reader 2), respectively. Chi square test showed a significant difference between deep learning- and radiologist-based diagnostic accuracy (reader 1 P?=?0.016, reader 2 P?=?0.008). CONCLUSIONS Deep learning diagnostic model with FDG-PET imaging data can be useful as one of supportive tools to determine the HPV status in patients with OPSCC. PURPOSE Patients with hematuria and renal colic often undergo CT scanning. The purpose of our study was to assess variations in CT protocols and radiation doses for evaluation of hematuria and urinary stones in 20 countries. METHOD The International Atomic Energy Agency (IAEA) surveyed practices in 51 hospitals from 20 countries in the European region according to the IAEA Technical cooperation classification and obtained following information for three CT protocols (urography, urinary stones, and routine abdomen-pelvis CT) for 1276 patients patient information (weight, clinical indication), scanner information (scan vendor, scanner name, number of detector rows), scan parameters (such as number of phases, scan start and end locations, mA, kV), and radiation dose descriptors (CTDIvol, DLP). Two radiologists assessed the appropriateness of clinical indications and number of scan phases using the ESR Referral Guidelines and ACR Appropriateness Criteria. Descriptive statistics and Student's t tests were performed. RESULTS Most institutions use 3-6 phase CT urography protocols (80 %, median DLP 1793-3618?mGy.cm) which were associated with 2.4-4.9-fold higher dose compared to 2-phase protocol (20 %, 740?mGy.cm) (p? less then ?0.0001). Likewise, 52 % patients underwent 3-5 phase routine abdomen- pelvis CT (1574-2945?mGy.cm) as opposed to 37 % scanned with a single-phase routine CT (676?mGy.cm). The median DLP for urinary stones CT (516?mGy.cm) were significantly lower than the median DLP for the other two CT protocols (p? less then ?0.0001). CONCLUSIONS Few institutions (4/13) use low dose CT for urinary stones. There are substantial variations in CT urography and routine abdomen-pelvis CT protocols result in massive radiation doses (up to 2945-3618?mGy.cm).